def test_dataapi():
start = 1502153929861
end = start + 10000
df = get_necklace('P108', start, end)
assert ((df.Time >= start).all() and (df.Time <= end).all())
assert_monotonic(df.Time.as_matrix())
N = df_segments.shape[0]
start_time = time.time()
for i in range(N):
if i % 1000 == 1:
print("Sample {}, elapsed {}".format(i, time.time() - start_time))
feature = pd.concat(list_total_feature)
feature.to_csv('feature_P120.csv', index=None)
start = datetime_to_epoch(df_segments['start'][i])
end = datetime_to_epoch(df_segments['end'][i])
try:
df = get_necklace('P120', start, end, reliability=0.01)
f = get_feature(df)
f['start'] = df_segments['start'][i]
f['end'] = df_segments['end'][i]
f['pmin'] = df_segments['pmin'][i]
f['pmax'] = df_segments['pmax'][i]
f['eps'] = df_segments['eps'][i]
f['length'] = df_segments['length'][i]
f['hour'] = df_segments['start'][i].hour
f['label'] = df_segments['chewing_gt'][i]
f['date_exp'] = df_segments['date_exp'][i]
list_total_feature.append(f)
except ValueError:
label_bite_path = "{}/{}/visualize/SYNC/{}/labelbites.json".format(
settings.CLEAN_FOLDER, subj, meal)
label_bite_dict, bite_epoch, bite_chewing_count = read_label_bite(
label_bite_path)
# create bite ranges (skip the range starting at 'e')
bite_ranges = []
b_previous = None
tmp = list(zip(bite_epoch, bite_epoch[1:]))
for interval, c in zip(tmp, bite_chewing_count):
if not 'e' in c:
bite_ranges.append(interval)
df = get_necklace(subj, bite_ranges[0][0], bite_ranges[-1][1])
peaks_index = peak_detection(df['proximity'], min_prominence=2)
peaks_time = df.index[peaks_index]
peaks_epoch = np.array([datetime_to_epoch(p) for p in peaks_time])
# calculating number of prominent peaks per bite
for b1, b2 in bite_ranges:
print(b1)
print(b2)
count = len(np.where((peaks_epoch >= b1) & (peaks_epoch < b2))[0])
print(count)
chewing_rate_list = []
chewing_rate_std_list = []
for m in valid_meals:
# get chewing groundtruth
gt = read_SYNC("{}/{}/visualize/SYNC/{}/labelchewing.json".format(
settings.CLEAN_FOLDER, subj, m))
print(gt)
rate_list = []
rate_std_list = []
for i in range(gt.shape[0]):
df = get_necklace(subj, datetime_to_epoch(gt['start'].iloc[i]),
datetime_to_epoch(gt['end'].iloc[i]))
print(df.shape)
if df.empty or df.shape[0] < 3:
print("EMPTY DATAFRAME, DOUBLE CHECK!!!")
continue
rate, rate_std = get_chewing_rate(df)
rate_list.append(rate)
rate_std_list.append(rate_std)
chewing_rate_list.append(np.mean(np.array(rate_list)))
chewing_rate_std_list.append(np.mean(np.array(rate_std_list)))